Automated Check Encoding Error Resolution

ABSTRACT

Aspects of the disclosure relate to enhanced check processing systems with improved check validation features and enhanced information security. A computing platform may determine whether a correlation between source data and metadata associated with a check exceeds a predetermined correlation threshold. Based on determining that the correlation does not exceed the predetermined correlation threshold, the computing platform may direct an OCR computing system to perform character recognition on the check. Then, the computing platform may determine whether a discrepancy between the metadata and an OCR output from the OCR computing system exceeds a predetermined resolution threshold. In response to determining that the discrepancy between the OCR output and the metadata does not exceed the predetermined resolution threshold, the computing platform may update stored records associated with the check. Subsequently, the computing platform may direct a DDA computing system to post a corrected payment associated with the check.

BACKGROUND

Aspects of the disclosure relate to enhanced check processing systemswith improved check validation features and enhanced informationsecurity. In particular, one or more aspects of the disclosure relate tocheck processing systems that utilize metadata and source dataassociated with checks to perform check validation, improve paymentaccuracy, and facilitate secure transactions.

Because many organizations and individuals rely on checks as a methodfor exchanging funds, ensuring the security and integrity of thecomputer systems used in processing check transactions is important. Inmany instances, however, it may be difficult to optimize the technicalperformance and operating efficiency of these computer systems whilealso ensuring that the security and integrity of these computer systemsis maintained.

SUMMARY

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with optimizing the performance of and ensuring thesecurity of check processing computer systems, along with theinformation that such systems may maintain, using enhanced validationtechniques.

In accordance with one or more embodiments, a computing platform havingat least one processor, a communication interface, and memory mayreceive, via the communication interface and from one or more computingplatforms, source data associated with a check. The computing platformmay also receive, from a first account management computing platform andvia the communication interface, a metadata output associated with thecheck. Then, the computing platform may determine whether a correlationbetween the source data and the metadata output exceeds a predeterminedcorrelation threshold. Based on determining that the correlation doesnot exceed the predetermined correlation threshold, the computingplatform may generate one or more commands directing an opticalcharacter recognition (OCR) computing system to perform characterrecognition on the check. Then, the computing platform may transmit, tothe OCR system and via the communication interface, the one or morecommands directing the OCR system to perform character recognition onthe check. Subsequently, the computing platform may receive, from theOCR system and via the communication interface, an OCR output. Then, thecomputing platform may determine whether a discrepancy between the OCRoutput and the metadata output exceeds a predetermined resolutionthreshold. In response to determining that the discrepancy between theOCR output and the metadata output does not exceed the predeterminedresolution threshold, the computing platform may update stored recordsassociated with the check. After updating the stored records associatedwith the check, the computing platform may generate one or more commandsdirecting a demand deposit account (DDA) computing system to post, basedon a correction of the discrepancy between the OCR output and themetadata output, a corrected payment associated with the check.Subsequently, the computing platform may transmit, to the DDA system andvia the communication interface, the one or more commands directing theDDA system to post the corrected payment associated with the check

In some embodiments, the computing platform may receive, from the DDAsystem and via the communication interface, a payment notificationindicating that a payment associated with the check has been posted.

In some embodiments, the computing platform may transmit, to the firstaccount management computing platform and via the communicationinterface, the payment notification. In addition, the computing platformmay transmit, to a second account management computing platform and viathe communication interface, the payment notification.

In some embodiments, the one or more computing platforms may comprise atleast one of the first account management computing platform and thesecond account management computing platform.

In some embodiments, the source data may comprise one or more of anissue file output from the second account management computing platform,an image cash letter (ICL) output, or a magnetic ink characterrecognition (MICR) output.

In some embodiments, the computing platform may receive, from one ormore computing systems, second source data associated with a secondcheck. Then, the computing platform may receive, from the first accountmanagement computing platform and via the communication interface, asecond metadata output associated with the second check. Subsequently,the computing platform may determine whether a correlation between thesecond source data and the second metadata output exceeds thepredetermined correlation threshold. Based on determining that thecorrelation exceeds the predetermined correlation threshold, thecomputing platform may generate one or more commands directing the DDAcomputing system to post a payment associated with the second check.Subsequently, the computing platform may transmit, to the DDA system andvia the communication interface, the one or more commands directing theDDA system to post the payment associated with the second check.

In some embodiments, posting the corrected payment associated with thecheck may include causing deposit, by the computing platform, of anamount associated with the check.

In some embodiments, the OCR output may comprise one or more of anamount of money, an account number, a check serial number, a plotlocation of particular data, a pixel count, and or payee name.

In some embodiments, the computing platform may receive, from one ormore computing systems, second source data associated with a secondcheck. The computing platform may also receive, from the first accountmanagement computing platform and via the communication interface, asecond metadata output associated with the second check. Then, thecomputing platform may determine whether a correlation between thesecond source data and the second metadata output exceeds thepredetermined correlation threshold. Based on determining that thecorrelation does not exceed the predetermined correlation threshold, thecomputing platform may generate one or more commands directing anoptical character recognition (OCR) computing system to performcharacter recognition on the second check. Subsequently, the computingplatform may transmit, to the OCR system and via the communicationinterface, the one or more commands directing the OCR system to performcharacter recognition on the second check. Then, the computing platformmay receive, from the OCR system and via the communication interface, asecond OCR output.

In some embodiments, the computing platform may determine whether adiscrepancy between the second OCR output and the second metadata outputexceeds the predetermined resolution threshold. Based on determiningthat the discrepancy between the second OCR output and the secondmetadata output exceeds the predetermined resolution threshold, thecomputing platform may generate a manual payment correctionnotification. In addition, the computing platform may generate one ormore commands directing an operator correction computing device to causedisplay of the manual payment correction notification. Then, thecomputing platform may receive, from the operator correction computingdevice, a manual payment correction confirmation. Subsequently, thecomputing platform may update, in response to receiving the manualpayment correction confirmation, stored records associated with thesecond check. After updating the stored records associated with thecheck, the computing platform may generate one or more commandsdirecting the DDA system to post, based on a correction of thediscrepancy between the second OCR output and the second metadataoutput, a corrected payment associated with the second check.Thereafter, the computing platform may transmit, to the DDA system andvia the communication interface, the one or more commands directing theDDA system to post the corrected payment associated with the secondcheck.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A and 1B depict an illustrative computing environment fordeploying an enhanced check processing system that utilizes improvedvalidation techniques in accordance with one or more exampleembodiments;

FIGS. 2A-2I depict an illustrative event sequence for deploying anenhanced check processing system that utilizes improved validationtechniques in accordance with one or more example embodiments;

FIGS. 3 and 4 depict example graphical user interfaces for deploying anenhanced check processing system that utilizes improved validationtechniques in accordance with one or more example embodiments; and

FIG. 5 depicts an illustrative method for deploying an enhanced checkprocessing system that utilizes improved validation techniques inaccordance with one or more example embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

FIGS. 1A and 1B depict an illustrative computing environment fordeploying an enhanced check processing system that utilizes improvedvalidation techniques in accordance with one or more exampleembodiments. Referring to FIG. 1A, computing environment 100 may includeone or more computer systems. For example, computing environment 100 mayinclude a first account management computing platform 102, a secondaccount management computing platform 103, a check validation computingplatform 104, a demand deposit account (DDA) computing system 105, andan optical character recognition (OCR) computing system 106.

First account management computing platform 102 may include one or morecomputing devices and/or other computer components (e.g., processors,memories, communication interfaces). In addition, and as illustrated ingreater detail below, first account management computing platform 102may be configured to generate, host, transmit, and/or otherwise provideone or more web pages and/or other graphical user interfaces (which may,e.g., cause one or more other computer systems to display and/orotherwise present the one or more web pages and/or other graphical userinterfaces). In some instances, the web pages and/or other graphicaluser interfaces generated by first account management computing platform102 may be associated with an internal portal provided by anorganization, such as a check processing management portal provided by afinancial institution. Such a portal may, for instance, provideemployees of the financial institution with access to financial accountinformation (e.g., account balance information, account statements,recent transaction history information, or the like) and/or may provideemployees of the financial institution with menus, controls, and/orother options to schedule and/or execute various transactions (e.g.,issuing checks, withdrawals, deposits, or the like).

Second account management computing platform 103 may include one or morecomputing devices and/or other computer components (e.g., processors,memories, communication interfaces). In addition, and as illustrated ingreater detail below, second account management computing platform 103may be configured to generate, host, transmit, and/or otherwise provideone or more web pages and/or other graphical user interfaces (which may,e.g., cause one or more other computer systems to display and/orotherwise present the one or more web pages and/or other graphical userinterfaces). In some instances, the web pages and/or other graphicaluser interfaces generated by second account management computingplatform 103 may be associated with an internal portal provided by anorganization, such as a check processing management portal provided by afinancial institution. Such a portal may, for instance, provideemployees of the financial institution with access to financial accountinformation (e.g., account balance information, account statements,recent transaction history information, or the like) and/or may provideemployees of the financial institution with menus, controls, and/orother options to schedule and/or execute various transactions (e.g.,issuing checks, withdrawals, deposits, or the like). In one or morearrangements, second account management computing platform 103 may beoperated by, used by, and/or otherwise associated with a secondfinancial institution different from a first financial institution thatmay operate, use, and/or otherwise be associated with first accountmanagement computing platform 102.

As illustrated in greater detail below, check validation computingplatform 104 may include one or more computing devices configured toperform one or more of the functions described herein. For example,check validation computing platform 104 may include one or morecomputers (e.g., laptop computers, desktop computers, servers, serverblades, or the like).

DDA computing system 105 may be a computer system that includes one ormore computing devices and/or other computer components (e.g.,processors, memories, communication interfaces). In addition, DDAcomputing system 105 may be configured to receive requests (e.g.,requests to process payments issued and/or received by the first accountmanagement computing platform 102 and the second account managementcomputing platform 103) from one or more remote computing devices and/orperform various functions with respect to such requests, as discussed ingreater detail below.

OCR computing system 106 may be a computer system that includes one ormore computing devices and/or other computer components (e.g.,processors, memories, communication interfaces). In addition, OCRcomputing system 106 may be configured to receive requests (e.g.,requests to perform optical character recognition on a check from anaccount management computing platform, such as first account managementcomputing platform 102, second account management computing platform103, and the like). In some instances, the OCR computing system 106 maydetermine source data from a face of a check. The OCR computing system106 may transmit the source data to the check validation computingplatform 104 for further analysis, as discussed in greater detail below.

Computing environment 100 also may include one or more networks, whichmay interconnect one or more of first account management computingplatform 102, second account management computing platform 103, checkvalidation computing platform 104, DDA computing system 105, and OCRcomputing system 106. For example, computing environment 100 may includea network 101 (which may, e.g., interconnect first account managementcomputing platform 102, second account management computing platform103, check validation computing platform 104, DDA computing system 105,and OCR computing system 106).

In one or more arrangements, first account management computing platform102, second account management computing platform 103, check validationcomputing platform 104, DDA computing system 105, OCR computing system106, and/or the other systems included in computing environment 100 maybe any type of computing device capable of receiving a user interface,receiving input via the user interface, and communicating the receivedinput to one or more other computing devices. For example, first accountmanagement computing platform 102, second account management computingplatform 103, check validation computing platform 104, DDA computingsystem 105, OCR computing system 106, and/or the other systems includedin computing environment 100 may, in some instances, be and/or includeserver computers, desktop computers, laptop computers, tablet computers,smart phones, or the like that may include one or more processors,memories, communication interfaces, storage devices, and/or othercomponents. As noted above, and as illustrated in greater detail below,any and/or all of first account management computing platform 102,second account management computing platform 103, check validationcomputing platform 104, DDA computing system 105, OCR computing system106 may, in some instances, be special-purpose computing devicesconfigured to perform specific functions.

Referring to FIG. 1B, check validation computing platform 104 mayinclude one or more processors 111, memory 112, and communicationinterface 113. A data bus may interconnect processor 111, memory 112,and communication interface 113. Communication interface 113 may be anetwork interface configured to support communication between checkvalidation computing platform 104 and one or more networks (e.g.,network 101, or the like). Memory 112 may include one or more programmodules having instructions that when executed by processor 111 causecheck validation computing platform 104 to perform one or more functionsdescribed herein and/or one or more databases that may store and/orotherwise maintain information which may be used by such program modulesand/or processor 111. In some instances, the one or more program modulesand/or databases may be stored by and/or maintained in different memoryunits of check validation computing platform 104 and/or by differentcomputing devices that may form and/or otherwise make up checkvalidation computing platform 104. For example, memory 112 may have,store, and/or include a check validation module 112 a, a checkvalidation database 112 b, and a machine learning engine 112 c. Checkvalidation module 112 a may have instructions that direct and/or causecheck validation computing platform 104 to execute advanced checkvalidation techniques, as discussed in greater detail below. Checkvalidation database 112 b may store information used by check validationmodule 112 a and/or check validation computing platform 104 in checkvalidation and/or in performing other functions. Machine learning engine112 c may have instructions that direct and/or cause the checkvalidation computing platform 104 to perform check validation and toset, define, and/or iteratively refine optimization rules and/or otherparameters used by the check validation computing platform 104 and/orother systems in computing environment 100.

FIGS. 2A-2I depict an illustrative event sequence for deploying anenhanced check processing system that utilizes improved validationtechniques in accordance with one or more example embodiments. Referringto FIG. 2A, at step 201, first account management computing platform 102may receive an instruction to issue a check. For example, first accountmanagement computing platform 102 may receive an instruction to issue apersonal check, a business check, a reimbursement check, acorporate-issued paycheck, or the like from a user of first accountmanagement computing platform 102 (who may, e.g., be an employee oraffiliate of an organization operating and/or otherwise using firstaccount management computing platform 102).

At step 202, first account management computing platform 102 may issuethe check. For example, the first account management computing platform102 may cause a check to be printed. In printing and/or otherwiseissuing the check, first account management computing platform 102 maycause the check to indicate an amount of money to be transferred and anaccount into which the money should be transferred.

At step 203, first account management computing platform 102 maygenerate an issue file. For example, the first account managementcomputing platform 102 may generate an issue file that includesinformation indicating the amount of money to be transferred, theaccount into which the money should be transferred, a check number,and/or other information associated with the check.

At step 204, the first account management computing platform 102 maytransmit, to check validation computing platform 104, the issue filegenerated at step 203. For example, the first account managementcomputing platform 102 may establish a connection to check validationcomputing platform 104. For example, the first account managementcomputing platform 102 may establish a first wireless data connection tocheck validation computing platform 104 to link the first accountmanagement computing platform 102 to the check validation computingplatform 104. While the first wireless data connection is established,the first account management computing platform 102 may transmit, tocheck validation computing platform 104 and via the communicationinterface 113, the issue file.

Referring to FIG. 2B, at step 205, second account management computingplatform 103 may receive the check issued at step 201. In some examples,the first account management computing platform 102 may be associatedwith the same financial institution as second account managementcomputing platform 103. For example, the first account managementcomputing platform 102 may receive an instruction to issue a check foran account from a first financial institution and then the secondaccount management computing platform 103 may subsequently receive aninstruction to cause an amount indicated by the check to be depositedinto a different account associated with the first financialinstitution. In other examples, the first account management computingplatform 102 may be associated with a different financial institutionthan the second account management computing platform 103. For example,a the first account management computing platform 102 may receive aninstruction to issue a check for an account from the first financialinstitution and then the second account management computing platform103 may subsequently receive an instruction to cause an amount indicatedby the check to be deposited into a different account associated with asecond financial institution. The second account management computingplatform 103 may instructions to process a physical check (such as anactual paper check) or a virtual check (electronic fund transfer, animage of a check captured via a mobile banking application, or thelike).

At step 206, the second account management computing platform 103 mayreceive metadata associated with the check. In some instances, thesecond account management computing platform 103 may receiveinstructions to electronically scan the check to generate the metadata.In other instances, source data from the check may be entered via a userinterface generated, displayed, and/or otherwise provided by the secondaccount management computing platform 103. For example, the secondaccount management computing platform 103 may generate a user interfaceprompting a user (who may e.g., be an employee or affiliate of anorganization operating and/or otherwise using the second accountmanagement computing platform 103) to enter an account number, anamount, a check number, and the like associated with the check.

At step 207, the second account management computing platform 103 maygenerate an image cash letter (ICL). For example, the second accountmanagement computing platform 103 may generate the ICL using one of theremotely created checks (RCC) and X9.37 standards. For example, thesecond management computing platform 103 may generate a digitalreproduction of the check including information such as account number,amount, and check number associated with the check received at step 205.

At step 208, the second account management computing platform 103 maytransmit the metadata and the ICL to the check validation computingplatform 104. For example, the second account management computingplatform 103 may establish a connection to check validation computingplatform 104. For example, the second account management computingplatform 103 may establish a second wireless data connection to checkvalidation computing platform 104 to link the second account managementcomputing platform 103 to the check validation computing platform 104.While the second wireless data connection is established, the secondaccount management computing platform 103 may transmit, via thecommunication interface 113 and to check validation computing platform104, the metadata and the ICL.

Referring to FIG. 2C, at step 209, the check validation computingplatform 104 may receive the metadata and the ICL. For example, thecheck validation computing platform 104 may receive, from the secondaccount management computing platform 103, via the communicationinterface 113, and via the second wireless data connection, the metadataand the ICL. In some instances, the check validation computing platform104 may receive the metadata and the ICL prior to receiving the issuefile. In other instances, the check validation computing platform 104may receive the issue file prior to receiving the metadata and the ICL.

At step 210, check validation computing platform 104 may compare themetadata, received at step 209, to source data. For example, the checkvalidation computing platform 104 may compare the metadata to one ormore of the issue file, the ICL, magnetic ink character recognition code(MICR) data from the face of the check, and the like.

At step 211, the check validation computing platform 104 may determine,based on the comparison performed at step 210, if the metadata exceeds apredetermined correlation threshold. For example, if the metadataexceeds the predetermined correlation threshold, the check validationcomputing platform 104 may determine that the metadata matches thesource data, and that the payment should be posted. If the metadata doesexceed the predetermined correlation threshold, the check validationcomputing platform 104 may proceed to step 220 to post a paymentassociated with the check. In another example, if the metadata does notexceed the predetermined correlation threshold, the check validationcomputing platform 104 may determine that there is a discrepancy betweenthe metadata and the source data, and that the payment should be furtheranalyzed prior to posting. If the metadata does not exceed thepredetermined correlation threshold, the check validation computingplatform 104 may proceed to step 212. The check validation computingplatform 104 may determine if the metadata exceeds the predeterminedcorrelation threshold using, for example, machine learning analysis,algorithms, and datasets.

At step 212, check validation computing platform 104 may generate one ormore optical character recognition (OCR) commands. For example, at step212, based on determining that the metadata does not exceed thepredetermined correlation threshold, check validation computing platform104 may generate one or more OCR commands directing OCR computing system(e.g., OCR computing system 106) to perform optical characterrecognition on the check.

Referring to FIG. 2D, at step 213, check validation computing platform104 may transmit the one or more OCR commands to OCR computing system106. For example, at step 213, the check validation computing platformmay establish a connection to OCR computing system 106. For example, thecheck validation computing platform 104 may establish a third wirelessdata connection to OCR computing system 106 to link the check validationcomputing platform 104 to the OCR computing system 106. While the thirdwireless data connection is established, the check validation computingplatform 104 may transmit, via the communication interface 113 and toOCR computing system 106, the one or more OCR commands.

At step 214, OCR computing system 106 may receive the one or more OCRcommands. For example, the OCR computing system 106 may receive, fromthe check validation computing platform 104, via the communicationinterface 113, and via the third wireless data connection, the one ormore OCR commands.

At step 215, the OCR computing system 106 may generate, in response toreceiving the one or more OCR commands at step 214, the OCR output. Forexample, the OCR computing system 106 may perform optical characterrecognition on the check to determine the source data associated withthe check (check serial number, amount, account number, and the like).In another example, the OCR computing system 106 may determine checkpatterns, a check style, static check data, a plot location ofparticular check data, a pixel count, and a payee name associated withthe check. In yet another example, the OCR computing system 106 maygenerate a machine learning database of checks associated with variouspayers, payees, and/or payer-payee combinations. In this example, theOCR computing system 106 may build a profile of various checks, and maycompare, using machine learning algorithms and analysis, a check profileto other checks obtained from the machine learning database. This mayallow the OCR computing system 106 to determine whether a check lookslike others from a particular account.

At step 216, the OCR computing system 106 may transmit the OCR outputgenerated at step 215 to the check validation computing platform 104.For example, the OCR computing system 106 may transmit, to the checkvalidation computing platform 104, via the communication interface 113,and via the third wireless data connection, the OCR output.

Referring to FIG. 2E, at step 217, the check validation computingplatform 104 may receive the OCR output transmitted at step 216. Forexample, the check validation platform 104 may receive, from the OCRcomputing system 106, via the third wireless data connection, and viathe communication interface 113, the OCR output.

At step 218, the check validation computing platform 104 may determinewhether a discrepancy between the OCR output and the metadata exceeds apredetermined resolution threshold. For example, the check validationcomputing platform 104 may determine whether there is a discrepancybetween one or more of an account number, a payment amount, and a checknumber indicated by the OCR output and the metadata. Alternatively oradditionally, the check validation computing platform 104 may utilizemachine learning algorithms and analysis to determine whether thediscrepancy between the OCR output and the metadata exceeds apredetermined resolution. For example, the OCR computing system 106 maycollect check sensor data from different checks associated with aplurality of payers. The OCR computing system 106 may collect the checksensor data by performing optical character recognition on a pluralityof checks. For example, the OCR computing system 106 may receiveinstructions to determine an account number, a payment amount, a checknumber, check patterns, a check style, static check data, a plotlocation of particular check data, a pixel count, a payee nameassociated with the check, and the like. Based on the check sensor data,the check validation computing platform 104 may determine machinelearning datasets that may link a plurality of check features to aparticular payer. Then, when a new check is received, the OCR computingsystem 106 may perform optical character recognition on the new check todetermine check sensor data associated with that new check. The checkvalidation computing platform 104 may compare the new check sensor datato the machine learning datasets. For example, the check validationcomputing platform 104 may implement machine learning algorithms todetermine whether the new check sensor data matches one or more machinelearning datasets to a degree that exceeds a predetermined correlationthreshold. For example, the check validation computing platform 104 mayimplement at least one of decision tree learning, association rulelearning, artificial neural networks, deep learning, inductive logicprogramming, support vector machines, clustering, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, genetic algorithms, rule basedmachine learning, regression, and the like. In these examples, if thecheck validation computing platform 104 determines that a payer name onthe new check does not match a check profile associated with the newcheck, the check validation computing platform 104 may flag the checkfor further review.

For example, if the discrepancy between the OCR output and the metadatadoes exceed the predetermined resolution threshold, the check validationcomputing platform 104 may generate, based on the determination that thediscrepancy between the OCR output and the metadata output exceeds thepredetermined resolution threshold, a manual payment correctionnotification. For example, the check validation computing platform 104may determine that the OCR output and the metadata are a match, but thatthe check was previously flagged by the check validation computingplatform 104 due to a discrepancy between the metadata and source datafrom the MICR line of the check. In another example, the checkvalidation computing platform 104 may determine, as described above,that a check profile does not match a payer name. In these examples, thecheck validation computing platform may determine that encoding of thecheck may have been tampered with or that the check may be anunauthorized reproduction. The check validation computing platform 104may generate one or more commands directing an operator correctioncomputing device to cause display of the manual payment correctionnotification. In these examples, the check validation computing platform104 may generate the manual payment correction notification. Forexample, the operator correction computing device may receive manualpayment correction interface information (which may, e.g., include userinterface templates, user interface layouts, user interface contentdata, and/or other information). The operator correction computingdevice may cause display, in response to the one or more commands fromthe check validation computing platform 104, of the manual paymentcorrection notification. In causing display of the manual paymentcorrection user interface, the operator correction computing device maydisplay and/or otherwise present a graphical user interface similar tographical user interface 305, which is illustrated in FIG. 3. As seen inFIG. 3, graphical user interface 305 may include the manual paymentcorrection notification and/or other user-selectable options and/orcontent. Once the manual correction has been made, the check validationcomputing platform 104 may receive, from the operator correctioncomputing device, a manual payment correction confirmation. Once themanual payment correction confirmation is received, the check validationcomputing platform 104 may proceed to step 219.

If the discrepancy between the OCR output and the metadata does notexceed the predetermined resolution threshold, the check validationcomputing platform 104 may proceed to step 219 to correct thediscrepancy. For example, the check validation computing platform 104may determine that although the physical check is numbered 1234, themetadata indicates that the check is numbered 1235. In this example, thecheck validation computing platform 104 may determine that thediscrepancy between the OCR output and the metadata does not exceed thepredetermined resolution threshold. In another example, the checkvalidation computing platform 104 may determine that the metadataindicates a $90 payment and the OCR output indicates a $100 payment. Inthis example, the check validation computing platform 104 may similarlydetermine that the discrepancy between the OCR output and the metadatadoes not exceed the predetermined resolution threshold, and may correctthe payment to $100 prior to posting.

At step 219, the check validation computing platform 104 may updatestored records to correct the discrepancy. For example, the checkvalidation computing platform 104 may update at least one of a checknumber, an account number, or an amount in a stored record associatedwith the check.

At step 220, once the metadata has been verified or the stored recordshave been corrected, the check validation computing platform maygenerate one or more commands directing a DDA computing system to postthe payment associated with the check.

Referring to FIG. 2F, at step 221, the check validation computingplatform 104 may transmit the one or more commands to post the paymentto the DDA computing system 105. For example, at step 221, the checkvalidation computing platform may establish a connection to DDAcomputing system 105. For example, the check validation computingplatform 104 may establish a fourth wireless data connection to DDAcomputing system 105 to link the check validation computing platform 104to the DDA computing system 105. While the fourth wireless dataconnection is established, the check validation computing platform 104may transmit, via the communication interface 113 and to DDA computingsystem 105, the one or more commands to post the payment. In someexamples, if the stored records associated with the check were updated,the one or more commands may comprise commands to post a correction tothe payment.

At step 222, the DDA computing system 105 may receive the one or morecommands to post the payment. For example, the DDA computing system 105may receive, from the check validation computing platform 104, via thefourth wireless data connection, and via the connection interface, theone or more commands to post the payment.

At step 223, the DDA computing system 105 may post, in response to theone or more commands received at step 222, the payment. For example, theDDA computing system 105 may cause deposit of a correct amountassociated with the check. The DDA computing system 105 may also causethe deposit of the check to post to a user's checking account.

At step 224, the DDA computing system 105 may generate a paymentnotification. For example, the payment notification may comprise anindication that a correct payment has posted, and may show thediscrepancy between an original payment and the correct payment.

Referring to FIG. 2G, at step 225, DDA computing system 105 may transmitthe payment notification to the check validation computing platform 104.For example, the DDA computing system 105 may transmit, to the checkvalidation computing platform 104, via the fourth wireless dataconnection, and via the communication interface 113, the paymentnotification.

At step 226, the check validation computing platform 104 may receive thepayment notification transmitted at step 225. For example, the checkvalidation computing platform 104 may receive, from the DDA computingsystem, via the communication interface 113, and via the fourth wirelessconnection, the payment notification. For example, the check validationcomputing platform 104 may receive an indication that the correctpayment has posted, user interface templates, user interface layouts,user interface content data, and/or other information.

At step 227, the check validation computing platform 104 may causedisplay of the payment notification (e.g., based on the informationreceived from the DDA computing system 105). For example, the checkvalidation computing platform 104 may display and/or otherwise present agraphical user interface similar to graphic user interface 405, which isillustrated in FIG. 4. As seen in FIG. 4, graphical user interface 405may include previously determined source data associated with the checkalongside revised source data associated with the check. In someinstances, if a correction to the source data was not made, thegraphical user interface 405 may indicate source data associated withthe check without showing a revised payment. Although FIG. 4 shows anaccount number, a check number, and an amount associated with the check,it should be understood that other source data associated with the checkmay also be displayed via graphical user interface 405. For example,graphical user interface 405 may include source data such as a checkstyle, static check data, a plot location of particular check data, apixel count, and a payee name, a payer name, and the like.

At step 228, the check validation computing platform 104 may transmit,to the first account management computing platform 102, the paymentnotification received at step 226. For example, the check validationcomputing platform 104 may generate one or more commands directing thefirst account management computing platform 102 to cause display of thepayment notification. The check validation computing platform 104 maytransmit, to the first account management computing platform 102, theone or more commands. The check validation computing platform 104 maytransmit the one or more commands to the first account managementcomputing platform 102 via the communication interface 113, and via thefirst wireless data connection. The check validation computing platform104 may transmit the one or more commands along with the paymentnotification. For example, the check validation computing platform maytransmit, via the communication interface 113, to the first accountmanagement computing platform 102, and via the first wireless dataconnection, the payment notification.

Referring to FIG. 2H, at step 229, the first account managementcomputing platform 102 may receive the payment notification transmittedat step 228. For example, the first account management computingplatform 102 may receive, from the check validation computing platform104, via the communication interface 113, and via the first wirelessdata connection, the payment notification.

At step 230, the first account management computing platform 102 maycause display of the payment notification. For example, the firstaccount management computing platform 102 may cause display of thepayment notification based on the one or more commands to cause displayof the payment notification and the information received from the checkvalidation computing platform 104. In some instances, in causing displayof the correction notification, first account management computingplatform 102 may display and/or otherwise present a graphical userinterface similar to graphical user interface 405, which is illustratedin FIG. 4.

At step 231, the check validation computing platform 104 may transmitthe payment notification to second account management computing platform103. For example, the check validation computing platform 104 maygenerate one or more commands directing the second account managementcomputing platform 103 to cause display of the payment notification. Thecheck validation computing platform 104 may transmit, to the secondaccount management computing platform 103, the one or more commands. Thecheck validation computing platform 104 may transmit the one or morecommands to the second account management computing platform 103 via thecommunication interface 113, and via the second wireless dataconnection. The check validation computing platform 104 may transmit theone or more commands along with the payment notification. Actionsperformed at step 231 may be similar to those described above withregard to step 228.

At step 232, the second account management computing platform 103 mayreceive the payment notification transmitted at step 231. For example,the second account management computing platform may receive, from thecheck validation computing platform 104, via the second wireless dataconnection, and via the communication interface 113, the paymentnotification. Actions performed at step 232 may be similar to thosedescribed above with regard to step 229.

Referring to FIG. 2I, at step 233, the second account managementcomputing platform 103 may cause display of the payment notification.For example, the second account management computing platform 103 maycause display of the payment notification based on the one or morecommands to cause display of the payment notification and theinformation received from the check validation computing platform 104.In some instances, in causing display of the payment notification,second account management computing platform 103 may display and/orotherwise present a graphical user interface similar to graphical userinterface 405, which is illustrated in FIG. 4. Actions performed at step233 may be similar to those described above with regard to step 230.

Subsequently, the example event sequence may end, and check validationcomputing platform 104 may continue to validate checks in a similarmanner as discussed above (e.g., by comparing metadata associated withthe check to source data and OCR outputs associated with the check anddetermining if correlations exceed predetermined thresholds) toimplement check validation techniques. By operating in this way, checkvalidation computing platform 104 may improve security and accuracy ofpayments prior to posting by DDA computing system 105 and/or othersystems and devices included in computing environment 100. By performingOCR analysis prior to posting as payment, the check validation computingplatform 104 may ensure that a payment is correct prior to posting,rather than causing an incorrect payment to post and then fixing it. TheOCR analysis may also reduce the amount of manual check review to beperformed if a discrepancy between various types of check data isdetermined.

FIG. 5 depicts an illustrative method for deploying an enhanced checkprocessing system that utilizes improved validation techniques inaccordance with one or more example embodiments. Referring to FIG. 5, atstep 505, a computing platform having at least one processor, acommunication interface, and memory may receive, from a first accountmanagement computing platform and via a communication interface, anissue file associated with a check. At step 510, the computing platformmay receive, from a second account management computing platform and viathe communication interface, metadata and an ICL associated with thecheck. At step 515, after receiving the metadata, the computing platformmay compare the metadata to source data associated with the check. Inone instance, the source data may comprise the issue file. In anotherinstance, the source data may comprise the ICL. In yet another instance,the source data may be determined from a magnetic ink characterrecognition (MICR) line on the check.

At step 520, the computing platform may determine whether a correlationbetween the source data and the metadata exceeds a predeterminedcorrelation threshold. If the correlation does exceed the predeterminedcorrelation threshold, the computing platform may proceed to step 560 togenerate commands to post a payment associated with the check. If thecorrelation does not exceed the predetermined correlation threshold, thecomputing platform may proceed to step 525. At step 525, the computingplatform may generate one or more OCR commands. At step 530, thecomputing platform may transmit, to an OCR computing system and via thecommunication interface, the one or more OCR commands. At step 535, inresponse to transmitting the OCR commands, the computing platform mayreceive, from the OCR computing system and via the communicationinterface, an OCR output.

At step 540, the computing platform may determine whether the OCR outputexceeds a correction threshold. If the OCR output does not exceed thecorrection threshold, the computing platform may proceed to step 545. Ifthe OCR output does exceed the correction threshold, the computingplatform may proceed to step 550. At step 545, in response todetermining that the OCR output does not exceed a correction threshold,the computing platform may update stored records associated with thecheck to correct an error. At step 550, the computing platform maygenerate an operator correction output comprising a notification toinitiate manual error correction. At step 555, the computing platformmay transmit, to an operator correction computing device and via thecommunication interface, the operator correction output. In response tothe operator correction output, the computing platform may receive, fromthe operator correction computing device and via the communicationinterface, a confirmation that the error has been corrected. At step560, the computing platform may generate one or more commands to postthe payment. At step 565, the computing platform may transmit, to a DDAcomputing system and via the communication interface, the one or morecommands. At step 570, the computing platform may receive, in responseto transmitting the one or more commands, via the communicationinterface, and from the DDA computing system, a payment notificationindicating that a payment has posted. At step 575, the computingplatform may cause display of the payment notification. At step 580, thecomputing platform may transmit, to the account management computingplatforms and via the communication interface, the payment notification.The computing platform may generate one or more commands for the accountmanagement computing platforms to cause display of the paymentnotification, and may send the commands along with the paymentnotification.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,application-specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A computing platform, comprising: at least oneprocessor; a communication interface communicatively coupled to the atleast one processor; and memory storing computer-readable instructionsthat, when executed by the at least one processor, cause the computingplatform to: receive, from one or more computing platforms, source dataassociated with a check; receive, from a first account managementcomputing platform and via the communication interface, a metadataoutput associated with the check; determine whether a correlationbetween the source data and the metadata output exceeds a predeterminedcorrelation threshold; based on determining that the correlation betweenthe source data and the metadata output does not exceed thepredetermined correlation threshold, generate one or more commandsdirecting an optical character recognition (OCR) computing system toperform character recognition on the check; transmit, to the OCR systemand via the communication interface, the one or more commands directingthe OCR system to perform character recognition on the check; receive,from the OCR system and via the communication interface, an OCR output;determine whether a discrepancy between the OCR output and the metadataoutput exceeds a predetermined resolution threshold; in response todetermining that the discrepancy between the OCR output and the metadataoutput does not exceed the predetermined resolution threshold, updatestored records associated with the check; after updating the storedrecords associated with the check, generate one or more commandsdirecting a demand deposit account (DDA) computing system to post, basedon a correction of the discrepancy between the OCR output and themetadata output, a corrected payment associated with the check; andtransmit, to the DDA system and via the communication interface, the oneor more commands directing the DDA system to post the corrected paymentassociated with the check.
 2. The computing platform of claim 1, whereinthe memory stores additional computer-readable instructions that, whenexecuted by the at least one processor, further cause the computingplatform to: receive, from the DDA system and via the communicationinterface, a payment notification indicating that a payment associatedwith the check has been posted.
 3. The computing platform of claim 2,wherein the memory stores additional computer-readable instructionsthat, when executed by the at least one processor, further cause thecomputing platform to: transmit, to the first account managementcomputing platform and via the communication interface, the paymentnotification; and transmit, to a second account management computingplatform and via the communication interface, the payment notification.4. The computing platform of claim 3, wherein the one or more computingplatforms comprise at least one of the first account managementcomputing platform and the second account management computing platform.5. The computing platform of claim 4, wherein the source data comprisesone or more of an issue file output from the second account managementcomputing platform, an image cash letter (ICL) output, or a magnetic inkcharacter recognition (MICR) output.
 6. The computing platform of claim5, wherein the memory stores additional computer-readable instructionsthat, when executed by the at least one processor, further cause thecomputing platform to: receive, from the one or more computing systems,second source data associated with a second check; receive, from thefirst account management computing platform and via the communicationinterface, a second metadata output associated with the second check;determine whether a correlation between the second source data and thesecond metadata output exceeds the predetermined correlation threshold;based on determining that the correlation between the second source dataand the second metadata output exceeds the predetermined correlationthreshold, generate one or more commands directing the DDA computingsystem to post a payment associated with the second check; and transmit,to the DDA system and via the communication interface, the one or morecommands directing the DDA system to post the payment associated withthe second check.
 7. The computing platform of claim 1, wherein thememory stores additional computer-readable instructions that, whenexecuted by the at least one processor, further cause the computingplatform to post the corrected payment associated with the check bycausing deposit of an amount associated with the check.
 8. The computingplatform of claim 1, wherein the OCR output comprises one or more of anamount of money, an account number, a check serial number, a plotlocation of particular data, a pixel count, or a payee name.
 9. Thecomputing platform of claim 1, wherein the memory stores additionalcomputer-readable instructions that, when executed by the at least oneprocessor, further cause the computing platform to: receive, from theone or more computing systems, second source data associated with asecond check; receive, from the first account management computingplatform and via the communication interface, a second metadata outputassociated with the second check; determine whether a correlationbetween the second source data and the second metadata output exceedsthe predetermined correlation threshold; based on determining that thecorrelation between the second source data and the second metadataoutput does not exceed the predetermined correlation threshold, generateone or more commands directing an optical character recognition (OCR)computing system to perform character recognition on the second check;transmit, to the OCR system and via the communication interface, the oneor more commands directing the OCR system to perform characterrecognition on the second check; and receive, from the OCR system andvia the communication interface, a second OCR output.
 10. The computingplatform of claim 9, wherein the memory stores additionalcomputer-readable instructions that, when executed by the at least oneprocessor, further cause the computing platform to: determine whether adiscrepancy between the second OCR output and the second metadata outputexceeds the predetermined resolution threshold; and based on determiningthat the discrepancy between the second OCR output and the secondmetadata output exceeds the predetermined resolution threshold, generatea manual payment correction notification; generate one or more commandsdirecting an operator correction computing device to cause display ofthe manual payment correction notification; receive, from the operatorcorrection computing device, a manual payment correction confirmation;update, in response to receiving the manual payment correctionconfirmation, stored records associated with the second check; afterupdating the stored records associated with the check, generate one ormore commands directing the DDA system to post, based on a correction ofthe discrepancy between the second OCR output and the second metadataoutput, a corrected payment associated with the second check; andtransmit, to the DDA system and via the communication interface, the oneor more commands directing the DDA system to post the corrected paymentassociated with the second check.
 11. A method, comprising: at acomputing platform comprising at least one processor, a communicationinterface, and memory: receiving, by the at least one processor, via thecommunication interface, and from one or more computing platforms,source data associated with a check; receiving, from a first accountmanagement computing platform and via the communication interface, ametadata output associated with the check; determining whether acorrelation between the source data and the metadata output exceeds apredetermined correlation threshold; based on determining that thecorrelation between the source data and the metadata output does notexceed the predetermined correlation threshold, generating one or morecommands directing an optical character recognition (OCR) computingsystem to perform character recognition on the check; transmitting, tothe OCR system and via the communication interface, the one or morecommands directing the OCR system to perform character recognition onthe check; receiving, from the OCR system and via the communicationinterface, an OCR output; determining whether a discrepancy between theOCR output and the metadata output exceeds a predetermined resolutionthreshold; in response to determining that the discrepancy between theOCR output and the metadata output does not exceed the predeterminedresolution threshold, updating stored records associated with the check;after updating the stored records associated with the check, generatingone or more commands directing a demand deposit account (DDA) computingsystem to post, based on a correction of the discrepancy between the OCRoutput and the metadata output, a corrected payment associated with thecheck; and transmitting, to the DDA system and via the communicationinterface, the one or more commands directing the DDA system to post thecorrected payment associated with the check.
 12. The method of claim 11,further comprising: receiving, from the DDA system and via thecommunication interface, a payment notification indicating that apayment associated with the check has been posted.
 13. The method ofclaim 12, further comprising: transmitting, to the first accountmanagement computing platform and via the communication interface, thepayment notification; and transmitting, to a second account managementcomputing platform and via the communication interface, the paymentnotification.
 14. The method of claim 13, wherein the one or morecomputing platforms comprise at least one of the first accountmanagement computing platform and the second account managementcomputing platform.
 15. The method of claim 14, wherein the source datacomprises one or more of an issue file output from the second accountmanagement computing platform, an image cash letter (ICL) output, or amagnetic ink character recognition (MICR) output.
 16. The method ofclaim 15, wherein the memory stores additional computer-readableinstructions that, when executed by the at least one processor, furthercause the computing platform to: receiving, from the one or morecomputing systems, second source data associated with a second check;receiving, from the first account management computing platform and viathe communication interface, a second metadata output associated withthe second check; determining whether a correlation between the secondsource data and the second metadata output exceeds the predeterminedcorrelation threshold; based on determining that the correlation betweenthe second source data and the second metadata output exceeds thepredetermined correlation threshold, generating one or more commandsdirecting the DDA computing system to post a payment associated with thesecond check; and transmitting, to the DDA system and via thecommunication interface, the one or more commands directing the DDAsystem to post the payment associated with the second check.
 17. Themethod of claim 11, wherein posting the corrected payment associatedwith the check comprises causing deposit of an amount associated withthe check.
 18. The method of claim 11, wherein the OCR output comprisesone or more of an amount of money, an account number, a check serialnumber, a plot location of particular data, a pixel count, or a payeename.
 19. The method of claim 11, wherein the memory stores additionalcomputer-readable instructions that, when executed by the at least oneprocessor, further cause the computing platform to: determining that adiscrepancy between a second OCR output and a second metadata outputexceeds the predetermined resolution threshold, wherein the second OCRoutput and the second metadata output are associated with a secondcheck; based on determining that the discrepancy between the second OCRoutput and the second metadata output exceeds the predeterminedresolution threshold, generating a manual payment correctionnotification; generating one or more commands directing an operatorcorrection computing device to cause display of the manual paymentcorrection notification; receiving, from the operator correctioncomputing device, a manual payment correction confirmation; updating, inresponse to receiving the manual payment correction confirmation, storedrecords associated with the second check; after updating the storedrecords associated with the check, generating one or more commandsdirecting the DDA system to post, based on a correction of thediscrepancy between the second OCR output and the second metadataoutput, a corrected payment associated with the second check; andtransmitting, to the DDA system and via the communication interface, theone or more commands directing the DDA system to post the correctedpayment associated with the second check.
 20. One or more non-transitorycomputer-readable media storing instructions that, when executed by acomputing platform comprising at least one processor, a communicationinterface, and memory, cause the computing platform to: receive, fromone or more computing systems, source data associated with a check;receive, from a first account management computing platform and via thecommunication interface, a metadata output associated with the check;determine whether a correlation between the source data and the metadataoutput exceeds a predetermined correlation threshold; based ondetermining that the correlation between the source data and themetadata output does not exceed the predetermined correlation threshold,generate one or more commands directing an optical character recognition(OCR) computing system to perform character recognition on the check;transmit, to the OCR system and via the communication interface, the oneor more commands directing the OCR system to perform characterrecognition on the check; receive, from the OCR system and via thecommunication interface, an OCR output; determine whether a discrepancybetween the OCR output and the metadata output exceeds a predeterminedresolution threshold; in response to determining that the discrepancybetween the OCR output and the metadata output does not exceed thepredetermined resolution threshold, update stored records associatedwith the check; after updating the stored records associated with thecheck, generate one or more commands directing a demand deposit account(DDA) computing system to post, based on a correction of the discrepancybetween the OCR output and the metadata output, a corrected paymentassociated with the check; and transmit, to the DDA system and via thecommunication interface, the one or more commands directing the DDAsystem to post the corrected payment associated with the check.